rlDDPGAgentOptions

Options for DDPG agent

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Description

Use an rlDDPGAgentOptions object to specify options when creating a deep deterministic policy gradient (DDPG) agent. To create a DDPG agent, use rlDDPGAgent.

For more information, see Deep Deterministic Policy Gradient (DDPG) Agent.

For more information on the different types of reinforcement learning agents, see Reinforcement Learning Agents.

Creation

Syntax

opt = rlDDPGAgentOptions
opt = rlDDPGAgentOptions(Name=Value)

Description

opt = rlDDPGAgentOptions creates an options object for use as an argument when creating a DDPG agent using all default options. You can modify the object properties using dot notation.

opt = rlDDPGAgentOptions(Name=Value) creates the options object opt and sets its properties using one or more name-value arguments. For example, rlDDPGAgentOptions(DiscountFactor=0.95) creates an option set with a discount factor of 0.95. You can specify multiple name-value arguments.

example

Properties

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Sample time of the agent, specified as a positive scalar or as -1.

Within a MATLAB® environment, the agent is executed every time the environment advances, so, SampleTime does not affect the timing of the agent execution. If SampleTime is set to -1, in MATLAB environments, the time interval between consecutive elements in the returned output experience is considered equal to 1.

Within a Simulink® environment, the RL Agent block that uses the agent object executes every SampleTime seconds of simulation time. If SampleTime is set to -1 the block inherits the sample time from its input signals. Set SampleTime to -1 when the block is a child of an event-driven subsystem.

Set SampleTime to a positive scalar when the block is not a child of an event-driven subsystem. Doing so ensures that the block executes at appropriate intervals when input signal sample times change due to model variations. If SampleTime is a positive scalar, this value is also the time interval between consecutive elements in the output experience returned by sim or train, regardless of the type of environment.

If SampleTime is set to -1, in Simulink environments, the time interval between consecutive elements in the returned output experience reflects the timing of the events that trigger the RL Agent block execution.

This property is shared between the agent and the agent options object within the agent. If you change this property in the agent options object, it also changes in the agent, and vice versa.

Example: SampleTime=-1

Discount factor applied to future rewards during training, specified as a nonnegative scalar less than or equal to 1.

Example: DiscountFactor=0.9

Noise model options, specified as an OrnsteinUhlenbeckActionNoise or GaussianActionNoise object. For more information on the noise model, see Noise Model.

For an agent with multiple actions, if the actions have different ranges and units, it is likely that each action requires different noise model parameters. If the actions have similar ranges and units, you can set the noise parameters for all actions to the same value.

For example, for an agent with two actions, set the initial standard deviation of each action to a different value while using the same decay rate for both standard deviations.

opt = rlDDPGAgentOptions;
opt.NoiseOptions.StandardDeviation = [0.1 0.2];
opt.NoiseOptions.StandardDeviationDecayRate = 1e-4;

To use Gaussian action noise, first create a default GaussianActionNoise object. Then, specify any nondefault model properties using dot notation.

opt = rlDDPGAgentOptions;
opt.NoiseOptions = rl.option.GaussianActionNoise;
opt.NoiseOptions.StandardDeviation = 0.05;

Experience buffer size, specified as a positive integer. During training, the agent computes updates using a mini-batch of experiences randomly sampled from the buffer.

Example: ExperienceBufferLength=1e6

Size of random experience mini-batch, specified as a positive integer. During each training episode, the agent randomly samples experiences from the experience buffer when computing gradients for updating the critic properties. Large mini-batches reduce the variance when computing gradients but increase the computational effort.

Example: MiniBatchSize=128

Maximum batch-training trajectory length when using a recurrent neural network, specified as a positive integer. This value must be greater than 1 when using a recurrent neural network and 1 otherwise.

Example: SequenceLength=4

Actor optimizer options, specified as an rlOptimizerOptions object. It allows you to specify training parameters of the actor approximator such as learning rate, gradient threshold, as well as the optimizer algorithm and its parameters. For more information, see rlOptimizerOptions and rlOptimizer.

Example: ActorOptimizerOptions = rlOptimizerOptions(LearnRate=2e-3)

Critic optimizer options, specified as an rlOptimizerOptions object. It allows you to specify training parameters of the critic approximator such as learning rate, gradient threshold, as well as the optimizer algorithm and its parameters. For more information, see rlOptimizerOptions and rlOptimizer.

Example: CriticOptimizerOptions = rlOptimizerOptions(LearnRate=5e-3)

Number of future rewards used to estimate the value of the policy, specified as a positive integer. Specifically, ifNumStepsToLookAhead is equal to N, the target value of the policy at a given step is calculated adding the rewards for the following N steps and the discounted estimated value of the state that caused the N-th reward. This target is also called N-step return.

Note

When using a recurrent neural network for the critic, NumStepsToLookAhead must be 1.

For more information, see [1], Chapter 7.

Example: NumStepsToLookAhead=3

Minimum number of samples to generate before learning starts. Use this option to ensure that learning takes place over a more diverse data set at the beginning of training. The default, and minimum, value is the value of MiniBatchSize. After the software collects a minimum of NumWarmStartSteps samples, learning occurs at the intervals specified by the LearningFrequency property.

Example: NumWarmStartSteps=20

Number of times an agent learns over the data set stored in the experience buffer, specified as a positive integer. For off-policy agents that support this property (DQN, DDPG, TD3 and SAC), this value defines the number of passes over the data in the replay buffer at each learning iteration.

Example: NumEpoch=2

Maximum number of mini-batches used for learning during a single epoch, specified as a positive integer.

For off-policy agents that support this property (DQN, DDPG, TD3, and SAC), the actual number of mini-batches used for learning depends on the length of the replay buffer, and MaxMiniBatchPerEpoch specifies the upper bound. This value also specifies the maximum number of gradient steps per learning iteration because the maximum number of gradient steps is equal to the MaxMiniBatchPerEpoch value multiplied by the NumEpoch value.

For off-policy agents that support this property, a high MaxMiniBatchPerEpoch value means that more time is spent on learning than collecting new data. Therefore, you can use this parameter to control the sample efficiency of the learning process.

Example: MaxMiniBatchPerEpoch=200

Minimum number of environment interactions between learning iterations, specified as a positive integer or -1. This value defines how many new data samples need to be generated before learning. For DQN, DDPG, TD3, and SAC agents, the default value of -1 means that learning occurs after each episode is finished. Note that for these agents learning can start only after the software collects a minimum of NumWarmStartSteps samples. It then occurs at the intervals specified by the LearningFrequency property.

Example: LearningFrequency=4

Period of policy update with respect to critic update, specified as a positive integer. This option defines how often the actor is updated with respect to each critic update. For example, a value of 3 means that the actor is updated every three critic updates. Updating the actor less frequently than the critic can improve convergence at the cost of longer training times.

Example: PolicyUpdateFrequency=2

Smoothing factor for target actor and critic updates, specified as a positive scalar less than or equal to 1. For more information, see Target Update Methods.

Example: TargetSmoothFactor=1e-2

Number of steps between target actor and critic updates, specified as a positive integer. For more information, see Target Update Methods.

Example: TargetUpdateFrequency=5

Batch data regularizer options, specified as an rlBehaviorCloningRegularizerOptions object. These options are typically used to train the agent offline, from existing data. If you leave this option empty, no regularizer is used.

For more information, see rlBehaviorCloningRegularizerOptions.

Example: BatchDataRegularizerOptions = rlBehaviorCloningRegularizerOptions(BehaviorCloningRegularizerWeight=10)

Option for clearing the experience buffer before training, specified as a logical value.

Example: ResetExperienceBufferBeforeTraining=true

Options to save additional agent data, specified as a structure containing the following fields.

  • Optimizer

  • PolicyState

  • Target

  • ExperienceBuffer

You can save an agent object using one of these methods:

  • Use the save command.

  • Specify saveAgentCriteria and saveAgentValue in an rlTrainingOptions object.

  • Specify an appropriate logging function within a FileLogger object.

When you save an agent using any method, the fields in the InfoToSave structure determine whether the corresponding data saves with the agent. For example, if you set the PolicyState field to true, then the policy state saves along with the agent.

You can modify the InfoToSave property only after you create the agent options object.

Example: options.InfoToSave.Optimizer=true

Option to save the actor and critic optimizers, specified as a logical value. If you set the Optimizer field to false, then the actor and critic optimizers (which are hidden properties of the agent and can contain internal states) are not saved along with the agent, therefore saving disk space and memory. However, when the optimizers contains internal states, the state of the saved agent is not identical to the state of the original agent.

Example: true

Option to save the state of the explorative policy, specified as a logical value. If you set the PolicyState field to false, then the state of the explorative policy (which is a hidden agent property) is not saved along with the agent. In this case, the state of the saved agent is not identical to the state of the original agent.

Example: true

Option to save the actor and critic targets, specified as a logical value. If you set the Target field to false, then the actor and critic targets (which are hidden agent properties) are not saved along with the agent. In this case, when the targets contain internal states, the state of the saved agent is not identical to the state of the original agent.

Example: true

Option to save the experience buffer, specified as a logical value. If you set the PolicyState field to false, then the content of the experience buffer (which is accessible as an agent property using dot notation) is not saved along with the agent. In this case, the state of the saved agent is not identical to the state of the original agent.

Example: true

Object Functions

rlDDPGAgentDeep deterministic policy gradient (DDPG) reinforcement learning agent

Examples

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Open Live Script

Create an rlDDPGAgentOptions object that specifies the mini-batch size.

opt = rlDDPGAgentOptions(MiniBatchSize=48)
opt = 
  rlDDPGAgentOptions with properties:

                             SampleTime: 1
                         DiscountFactor: 0.9900
                           NoiseOptions: [1×1 rl.option.OrnsteinUhlenbeckActionNoise]
                 ExperienceBufferLength: 10000
                          MiniBatchSize: 48
                         SequenceLength: 1
                  ActorOptimizerOptions: [1×1 rl.option.rlOptimizerOptions]
                 CriticOptimizerOptions: [1×1 rl.option.rlOptimizerOptions]
                    NumStepsToLookAhead: 1
                      NumWarmStartSteps: 48
                               NumEpoch: 1
                   MaxMiniBatchPerEpoch: 100
                      LearningFrequency: -1
                  PolicyUpdateFrequency: 1
                     TargetSmoothFactor: 1.0000e-03
                  TargetUpdateFrequency: 1
            BatchDataRegularizerOptions: []
    ResetExperienceBufferBeforeTraining: 0
                             InfoToSave: [1×1 struct]

You can modify options using dot notation. For example, set the agent sample time to 0.5.

opt.SampleTime = 0.5;

Algorithms

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References

[1] Sutton, Richard S., and Andrew G. Barto. Reinforcement Learning: An Introduction. Second edition. Adaptive Computation and Machine Learning. Cambridge, Mass: The MIT Press, 2018.

Version History

Introduced in R2019a

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See Also

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